A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny Apps
Replication is a core principle for research, and the recent recognition of the importance of constructing prediction intervals for precise replications highlights the need for robust sample-size planning methodologies. However, methodological and technical complexities often hinder researchers from...
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| Format: | Article |
| Language: | English |
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PsychOpen GOLD/ Leibniz Institute for Psychology
2024-12-01
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| Series: | Methodology |
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| Online Access: | https://doi.org/10.5964/meth.13549 |
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| author | Wei-Ming Luh Jiin-Huarng Guo |
| author_facet | Wei-Ming Luh Jiin-Huarng Guo |
| author_sort | Wei-Ming Luh |
| collection | DOAJ |
| description | Replication is a core principle for research, and the recent recognition of the importance of constructing prediction intervals for precise replications highlights the need for robust sample-size planning methodologies. However, methodological and technical complexities often hinder researchers from efficiently achieving this task. This study addresses this challenge by developing five R Shiny apps specifically tailored to determine sample sizes concerning prediction intervals for the mean of the normal distribution. Two measures of precision, absolute and relative widths, are considered. Additionally, the apps consider unequal sampling unit costs and sample size allocations to achieve optimal results by exhaustive search. Simulation results validate the proposed methodology, demonstrating favorable coverage rates. Two illustrative examples of one-sample and two-sample problems showcase these apps’ versatility and user-friendly nature, providing researchers with a valid and straightforward approach for systematically planning sample sizes. |
| format | Article |
| id | doaj-art-169de260e2c0492c8ec0e0117160f3ec |
| institution | OA Journals |
| issn | 1614-2241 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PsychOpen GOLD/ Leibniz Institute for Psychology |
| record_format | Article |
| series | Methodology |
| spelling | doaj-art-169de260e2c0492c8ec0e0117160f3ec2025-08-20T02:12:38ZengPsychOpen GOLD/ Leibniz Institute for PsychologyMethodology1614-22412024-12-0120428330310.5964/meth.13549meth.13549A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny AppsWei-Ming Luh0Jiin-Huarng Guo1Institute of Education, National Cheng Kung University, Tainan City, Taiwan ROCDepartment of Applied Mathematics, National Pingtung University, Pingtung City, Taiwan ROCReplication is a core principle for research, and the recent recognition of the importance of constructing prediction intervals for precise replications highlights the need for robust sample-size planning methodologies. However, methodological and technical complexities often hinder researchers from efficiently achieving this task. This study addresses this challenge by developing five R Shiny apps specifically tailored to determine sample sizes concerning prediction intervals for the mean of the normal distribution. Two measures of precision, absolute and relative widths, are considered. Additionally, the apps consider unequal sampling unit costs and sample size allocations to achieve optimal results by exhaustive search. Simulation results validate the proposed methodology, demonstrating favorable coverage rates. Two illustrative examples of one-sample and two-sample problems showcase these apps’ versatility and user-friendly nature, providing researchers with a valid and straightforward approach for systematically planning sample sizes.https://doi.org/10.5964/meth.13549allocation ratioabsolute widthrelative widthsampling costoptimal sample size allocationreplicability |
| spellingShingle | Wei-Ming Luh Jiin-Huarng Guo A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny Apps Methodology allocation ratio absolute width relative width sampling cost optimal sample size allocation replicability |
| title | A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny Apps |
| title_full | A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny Apps |
| title_fullStr | A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny Apps |
| title_full_unstemmed | A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny Apps |
| title_short | A Framework for Planning Sample Sizes Regarding Prediction Intervals of the Normal Mean Using R Shiny Apps |
| title_sort | framework for planning sample sizes regarding prediction intervals of the normal mean using r shiny apps |
| topic | allocation ratio absolute width relative width sampling cost optimal sample size allocation replicability |
| url | https://doi.org/10.5964/meth.13549 |
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